- To self-learn from a few examples of given relations (and a large corpus), REPEL jointly optimize an embedding-based discriminator and a pattern-based generator.

- Both human annotators and external knowledge bases can provide weak supervision for information extraction tasks. Such heterogenous forms of weak supervisions trades off label quality with the amount of labeled data one can obtain. How could we leverage these heterogenous supervisions in a principled way?
- Indirection supervision may result in noisily- and partially-labeled data. This is especially challenging when dealing with a complex label space (e.g., a label hierarchy). We propose hierarchical partial-label embeddingn to overcome these issues.